ANALYZING PERFORMANCE PATTERNS AND USER EXPERIENCE IN ONLINE CYBERSECURITY CHALLENGES: INSIGHTS FROM SKRCTF

The International Journal of Multimedia & Its Applications (IJMA) Vol.16, No. 6, December 2024
DOI:10.5121/ijma.2024.16601 1
ANALYZING PERFORMANCE PATTERNS AND USER
EXPERIENCE IN ONLINE CYBERSECURITY
CHALLENGES: INSIGHTS FROM SKRCTF
Khoo Li Jing 12
, Maizatul Hayati Mohamad Yatim 1
, and Wong Yoke Seng 1
1
Faculty of Computing and Meta-Technology, Sultan Idris Education University,
Tanjong Malim, Malaysia
2
School of Creative Media and Computing, University of Wollongong Malaysia, Jalan
Kontraktor U1/14, Seksyen U1, 40150, Shah Alam, Selangor, Malaysia
ABSTRACT
The field of cybersecurity education has undergone a significant transformation, with the emergence of
innovative e-learning platforms that aim to bridge the gap between academic institutions and industry.
This study investigates the effectiveness of SKRCTF, a customized Capture-the-Flag platform designed to
support cybersecurity skill development, through comprehensive analysis of user performance patterns and
learning experiences. Drawing from data collected from 120 participants over a 12-week period, this
research employs mixed-methods analysis to examine learning progressions, engagement patterns, and
skill development pathways in cybersecurity education. The findings reveal three key insights: 1. the
emergence of distinct specialist and generalist learning patterns, challenging traditional assumptions
about linear skill progression; 2. the identification of an optimal engagement zone that maximizes learning
effectiveness; and 3. the discovery that analytical challenges serve as effective entry points for
cybersecurity skill development. The study also uncovers a complex relationship between perceived
difficulty and learning outcomes, where higher perceived difficulty often correlates with increased
persistence rather than decreased engagement. These insights contribute to both theoretical understanding
of cybersecurity skill development and practical platform design considerations. The research suggests
that effective cybersecurity education platforms should support multiple learning pathways, implement
structured engagement mechanisms, and carefully sequence challenges to optimize skill development.
These findings have significant implications for the design of cybersecurity education programs and the
development of future security professionals.
KEYWORDS
Cybersecurity education, e-learning, skill development, learning analytics, performance patterns
1. INTRODUCTION
This study investigates SKRCTF, a customized Capture-the-Flag platform designed to support
hands-on cybersecurity skill development. Existing cybersecurity training methods often struggle
to cater to the diverse skill levels and backgrounds of participants, leading to suboptimal learning
experiences [1].
1.1. Background and Research Context
The rise of cyber ranges has offered a more immersive and practical training approach in
Malaysian universities, yet their resource-heavy nature restricts widespread accessibility [2]. This
observation led us to explore more accessible alternatives such as CTF-style environments [3][4].

2
These platforms combine practical challenges with educational scaffolding, enabling learners to
develop technical competencies in a controlled environment. However, current research shows
limited evidence of their effectiveness in developing threat recognition and pattern identification
skills [5].
We designed SKRCTF to address these gaps, offering a customizable e-learning platform that
adapts to diverse learner backgrounds and skill levels. The key features of SKRCTF include:
● Gamified learning that simulates real-world scenario in a safe environment.
● Scaffolding and guided learning by providing structured learning paths and progressive
challenge difficulties.
● Adaptive and personalized learning with data-driven performance tracking.
● Community-driven learning via peer-to-peer learning through discussion forums.
1.2. Platform Overview and Scope
SKRCTF encompasses the typical topics in cybersecurity education. The 12 categories were
warm up tutorials, web security, cryptography, digital forensics, software reverse engineering
(RE), binary exploitation, Open-Source Intelligence (OSINT), mini games, programming,
steganography and miscellaneous. Each challenge category contains varying difficulty levels,
with a total of 127 challenges. This study examines data from 120 participants aged 19-30 who
engaged with the platform over a 12-week period.
1.3. Research Objectives and Questions
Previous research on the SKRCTF platform found limited evidence of learners recognizing
cybersecurity threats and cryptography patterns, despite their involvement [6]. This study now
shifts focus to investigating factors influencing learner motivation in the Malaysian cybersecurity
education context. The research questions are:
⒈ What factors contribute to the variability in performance between high and low scorers?
⒉ How does perceived challenge difficulty relate to actual performance?
⒊ What demographic factors influence performance across challenge categories?
⒋ How do user expectations align with their platform experience?
⒌ What patterns emerge in challenge completion rates across different user groups
2. METHODOLOGY
To address these research questions, this study employed a mixed-method approach, combining
quantitative and qualitative data analysis.
2.1. Data Collection
We collected survey data from learners using SKRCTF to train cybersecurity skills. We included
a survey question as part of the challenges to measure the learners’ perception and performance
level. The study duration is between 1st August 2024 to 18th September 2024. Ethical
considerations, such as data anonymization and informed consent, were followed.

3
2.1.1. Survey Design and Distribution
The survey consisted of four sections: demographic information, performance self-assessment,
challenge difficulty perception, and learning strategy preferences. The survey was distributed
after participants attempted 20 challenges through the SKRCTF platform, and participants were
incentivized to complete the survey with points and virtual badges.
2.1.2. Performance Data Collection
The researcher recorded the participants' performance data, including the number of challenges
completed, number of challenge categories completed, and total attempts taken to complete each
challenge.
2.2. Data Analysis Techniques
Descriptive statistics were employed to summarize participant demographics, challenge
completion rates, and overall performance metrics. Correlation analysis, specifically Pearson's
correlation coefficient, was used to examine relationships between perceived difficulty and
performance, as well as inter-category performance correlations. Comparative analysis was
conducted to assess performance differences across demographic groups and difficulty perception
categories. Frequency analysis and cross-tabulation were utilized to examine the distribution of
responses across various categorical variables.
Thematic analysis was employed to categorize and interpret participants' suggestions for platform
improvement. Content analysis was used to identify recurring patterns and themes in participants'
feedback and experiences.
3. FINDINGS
The collected data and analysis, based on the 5 research questions, led to the identification of 2
main themes: 1) Progressive learning patterns; and 2) Performance-Engagement dynamics. The
research questions were then mapped to these developed themes.
3.1. Progressive Challenge Design and Skill Development
The data showed that the learning curve on the SKRCTF platform was not linear but follow
distinct patterns across challenge categories and participant types. Analysis on the completion
aretes and engagement patterns led to 3 key findings: natural learning progression, specialized
skill development paths, and optimal engagement zones.
3.1.1. Natural Learning Progression
Completion rates across challenge categories revealed a clear skill progression hierarchy. The
categories difficulty tiers are: 1. Entry level (>40% average completion rate): Warm up (54.46%),
OSINT (83.33%), Forensics (47.19%), Linux (44.64%); 2. Intermediate Level (25-40% average
completion): Web (29.83%), Crypto (33.51%), Steganography (35.42%), Binary (29.17%); 3.
Advanced Level (<25% average completion): Programming (12.50%), Misc (14.17%).

4
Figure 1. Challenge distribution and difficulty level.
Increased practice attempts do not directly translate to greater learning effectiveness, shown in
Figure 2. The scatter plot shows a weak positive correlation between the number of attempts and
the completion rate. Even among participants with similar attempt counts, there is substantial
variability in their completion rates. Interestingly, the highest average completion rate of 52.1%
is observed within the 101-150 attempt range. This aligns with prior research [7], which indicates
that the effectiveness of learning cybersecurity skills is enhanced by an environment that
encourages active participation and structured learning, rather than just repetitive practice.
Figure 2. Average completion rate against category size
The number of challenges attempted decreases as difficulty increases, suggesting a natural
learning curve [8][9]. Learners tend to be more successful in completing entry-level challenges
compared to advanced ones. Beginning with easier challenges can help build learners'
confidence, which may then lead to increased engagement and attempts at higher-difficulty
challenges.
The challenge categories reveal a weak negative correlation between category size and
completion rates. Larger categories tend to have lower average completion rates, though some
exceptions exist. Categories with many challenges show this negative correlation, while medium-

5
sized categories have varied completion rates. Further analysis based on prior research [10], the
categories can be classified into three types: Technical, Analytical, and Mixed. Figure 3 presents
the new group type and their distribution of difficulty and completion rates.
Figure 3. Challenge categories and completion rate for each difficulty level
The Technical category, which includes Web Exploitation, Reverse Engineering, Binary
Exploitation, and Programming, exhibited the lowest average completion rates (27.98%). In
contrast, the Analytical category, such as Digital Forensics, Open-Source Intelligence,
Cryptography, and Steganography, demonstrated the highest completion rates (46.74%). The
remaining Mixed category, requiring cross-domain skills including Warm-up, Linux, Mini
Games, and Miscellaneous, had an average 36.70% completion rate.
The Analytical and Mixed categories have higher completion rates for beginners at 42.86% and
57.14% respectively, but the rates gradually decline at higher difficulty levels. This suggests that
the entry points for these categories effectively engage learners but may require better support for
progression. In contrast, the Technical category has a lower beginner completion rate but higher
rates at the intermediate level, showing a more consistent progression through higher difficulty
levels. This indicates that the Technical category builds upon foundational skills with steady
development, providing an effective learning curve for more experienced participants, but may be
more intimidating for beginners. [11].
The relationship between challenge type and completion rates offers valuable insights for
designing learning progressions in cybersecurity education. This pattern suggests that analytical
challenges provide better entry points for beginners, while technical challenges require more
structured scaffolding but enable consistent skill development [12][13].
3.1.2. Specialist and Generalist Development
Analysis of individual learning trajectories revealed two distinct learner profiles: Specialists
(n=62), who achieved high performance with over 70% completion in specific categories; and
Generalists (n=46) maintained consistent performance of over 40% completion across multiple
categories. The remaining 54% displayed a mix of performance patterns.

6
Diving deeper into each performance characteristics, Specialists required an average of 1.92
attempts per success in their dominating category while Generalists averaged 1.85 attempts per
success across all categories. These patterns suggest differences in their learning strategies [14]
[15]. Specialists may focus on developing deep expertise within a particular domain, while
Generalists adopt a broader, cross-disciplinary approach. Specialists tend to have higher success
rates in advanced challenges within their specialty, while Generalists may exhibit greater
adaptability across different challenge types. Recognizing these distinct learner profiles can guide
the design of personalized learning pathways and support mechanisms, moving beyond a one-
size-fits-all approach.
3.2. Performance-Engagement Dynamics
Analysis reveals a complex relationship between participants' perceived difficulty, engagement
patterns, and actual performance on the SKRCTF platform. Perceived difficulty reliably indicates
performance while revealing interesting engagement and persistence patterns.
3.2.1. Perceived Difficulty-Performance Patterns
The correlation between perceived challenge difficulty and performance exhibited significant yet
nuanced patterns. A moderate negative correlation (-0.421) emerged between perceived difficulty
and average scores, with performance varying by difficulty perception: "Easy peasy" had the
highest average overall completion (36.75%); "Somehow challenging" performed second-best
(31.52%); "Quite challenging" group had an average performance of 27.55%; "Very challenging"
group had the lowest average performance (22.92%).
However, perceived difficulty showed unexpected relationships with engagement. Participants
rating challenges as "Very challenging" made significantly more attempts (average 107.4)
compared to those rating them "Easy peasy" (average 72.9). This suggests that higher perceived
difficulty often motivated deeper engagement rather than deterring participation. This aligns with
the learning progression where practitioners often start with a broad approach before gradually
specializing [16]. As these participants gain more experience, their self-awareness and
metacognitive abilities improve, leading to a better alignment between their perceived difficulty
and actual performance.
3.2.2. Optimal Engagement Zones
The weak positive correlation between attempts and completion rates combined with the
variability in success rates among participants with similar attempt counts, suggests that effective
learning depends more on approach strategy than persistence alone. This aligns with findings
from [17] about the importance of balancing challenge and skill for optimal engagement.
Figure 4 shows that the 101-150 attempt range is the most optimal engagement zone across the
127 total challenges, achieving 52.1% completion rate. This finding challenges the assumption
that more practice automatically leads to better outcomes. The data indicates that the importance
of structured, measured practice yields better results over raw attempt numbers. Building on prior
research [18], the different challenge categories exhibited distinct patterns in learner engagement.
Technical challenges demonstrated lower overall completion rates but consistent progression
through increasing difficulty levels, while analytical challenges had higher initial accessibility but
varying progression patterns.

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Figure 4. Attempt vs completion rate scatter plot
Different challenge types require tailored engagement strategies. This indicates a need for more
effective category-specific learning strategies than uniform approaches. The data suggests
diminishing returns after certain thresholds, highlighting the importance of designing engagement
mechanisms that encourage regular, measured practice rather than intense bursts. Harnessing
these insights, learning platforms can integrate adaptive analytics to steer learners towards more
productive practice strategies and better long-term outcomes.
4. DISCUSSION
The data-driven insights gathered from the SKRCTF platform can help inform the development
of innovative e-learning models optimized for effective cybersecurity skill acquisition. Though
the 5 research questions did not uncover notable distinctions in demographic factors, we explore
the pivotal theoretical insights and practical applications of the findings, with a view toward the
broader vision of enhancing cybersecurity education framework development.
4.1. Theoretical Implications
Research in technical education has demonstrated the potential of Capture-the-Flag activities to
improve student engagement and self-efficacy in computing domains [14] [1]. Our findings
extend this understanding by revealing how the SKRCTF platform supports different learning
pathways while fostering sustained engagement. The complex relationship between perceived
difficulty and learning outcomes suggests a "confidence-competence spiral" where initial success
in general skill categories, particularly analytical challenges, builds the foundation for tackling
more technical domains.
The identification of distinct specialist and generalist learning patterns offers a novel conceptual
framework for comprehending skill development in cybersecurity skill development. This dual-
path framework challenges traditional models of linear progression [19][20], suggesting that
effective platforms must support both deep domain expertise and broad skill acquisition. The
divergent attempt patterns observed between specialists and generalists suggest fundamentally
diverse learning strategies, challenging the conventional one-size-fits-all approach in
cybersecurity education.

8
Our analysis challenges the assumption on increased practice automatically leads to improved
performance in cybersecurity education. The optimal engagement zone suggests a more complex
relationship between effort and achievement. The stark difference in completion rates between
technical and analytical categories reveals a fundamental distinction in how these skills are
acquired with analytical skills potentially laying the foundation to technical competency. This
insight is significant given that participants who perceived challenges as more difficult showed
higher persistence, serving as a motivator rather than a deterrent in self-directed learning
environment.
4.2. Practical Implication
The findings from SKRCTF provide concrete guidance for designing cybersecurity learning
platforms. The clear differentiation in completion rates between technical and analytical
categories suggests that platforms should intentionally sequence challenges to build learner
confidence. Starting with analytical challenges that demonstrate higher accessibility before
introducing technical challenges can create more progressions while building learner confidence.
Entry point planning across each cybersecurity challenge category is crucial. Well-designed
introductory challenges showed more consistent engagement patterns, suggesting that careful
attention to initial challenge design is crucial for learner retention. This extends beyond simple
difficulty ratings to include consideration of required background knowledge and skill
prerequisites. The distinct engagement patterns between specialists and generalists underscore the
need for flexible support systems. Specialized deep-dive resources should be available for
specialists, while generalists should be provided with broader contextual connections to enhance
their learning.
The optimal engagement zone could be a transformative guide for instructional design,
suggesting a need for more sophisticated gamification beyond basic points and badges in
cybersecurity education. Platforms should implement analytics-driven mechanisms on supporting
sustained, meaningful engagement rather than maximum attempt numbers. Understanding the
relationship between perceived difficulty and engagement patterns can help determine
appropriate reward structures, support diverse engagement styles, provide meaningful feedback,
and encourage strategic practice patterns.
4.3. Educational Framework Development
SKRCTF and associated research findings contribute to the emerging field of cybersecurity
education by providing a data-driven framework for designing effective e-learning systems.
The identification of distinct specialist and generalist learning patterns [18] [8] offers a novel
conceptual model for understanding skill development in cybersecurity education. The observed
progression from analytical to technical challenges aligns with established educational principles
of scaffolded learning while addressing practical cybersecurity skills.
A proposed integration framework includes:
 Using analytical challenges (forensics, cryptography) to introduce core concepts
 Gradually incorporating technical challenges (web security, reverse engineering) to build
practical skills
 Leveraging OSINT and steganography challenges to develop investigative thinking
 Employing binary exploitation and programming challenges for advanced skill
development

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A balanced skill development model recognizes that effective cybersecurity education must foster
both depth and breadth of knowledge. The SKRCTF case study showcases how data-driven
insights can inform the design of flexible, adaptive curricula to cater to the diverse needs of
learners. The framework features core components starting with foundational skills common to
all tracks, as well as specialized deep-dive opportunities. It also incorporates cross-domain
integration projects with industry-aligned challenge scenarios. This underscores the importance
of enabling learners to develop along their natural inclinations while ensuring baseline
competency across essential domains.
Despite the current SKRCTF platform having limited sample size and demographic constraints,
we propose an integrated assessment framework that combines formative and summative
evaluations based on the platform's engagement patterns and performance metrics. Analyzing
learners' attempt patterns and problem-solving approaches across different skill domains can
provide meaningful feedback to both educators and learners, informing their respective
improvement efforts.
While maintaining pedagogical rigor, bridging academic learning to industry needs remains a
critical priority. Besides mapping challenges to learning outcomes and producing metrics for skill
evaluation, developing industry-relevant tools and techniques, supporting continuous professional
development are paramount in preparing the next generation of cybersecurity professionals.
5. LIMITATIONS AND FUTURE RESEARCH
The preliminary research conducted on the SKRCTF platform represents an initial step in
understanding how adaptive e-learning can transform cybersecurity education. Limitations
include the relatively small sample size, limited time frame, homogeneous demographic, self-
reported difficulty perception.
To further enhance the SKRCTF platform, future research should focus on expanding the range
of learners, incorporating diverse challenge types, and conducting longitudinal studies to track
skill development over time. While SKRCTF provides a robust foundation for cybersecurity
education, certain platform-specific limitations should be acknowledged, such as the distribution
of challenges across categories, the need for a more sophisticated implementation of scaffolding
mechanisms, and the development of more nuanced assessment metrics.
Several promising research directions arise from this work, including learning analytics [19],
cross-platform integration, and cognitive load analysis. These areas could warrant further
investigation into adaptive learning systems, team learning dynamics, industry impact, and
cultural factors. Specifically, future research could explore the application of learning analytics to
gain deeper insight into specialist-generalist learning patterns and performance on the SKRCTF
platform. Cross-platform integration could enable the sharing of resources and best practices
across different cybersecurity education initiatives, fostering a more collaborative and
comprehensive approach to curriculum development [20]. Cognitive load analysis could provide
valuable insights into the relationship between perceived difficulty, actual complexity, and
learning outcomes, informing the design of more effective instructional strategies and challenge
sequences [21]. Additionally, investigations into team learning dynamics and the impact of
cultural factors on cybersecurity education could lead to the development of more inclusive and
supportive learning environments. Exploring these research directions could significantly
enhance the design and implementation of adaptive learning systems for the cybersecurity field.

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6. CONCLUSION
This study provides valuable insights into the design and implementation of effective
cybersecurity education platforms through a comprehensive analysis of the SKRCTF platform.
The research reveals several key findings that contribute to our understanding of how learners
engage with and develop cybersecurity skills in an online environment.
First, the identification of distinct learning patterns, particularly the emergence of specialist and
generalist approaches, challenges the traditional assumptions about linear skill progression in
cybersecurity education. This suggests that effective platforms must support multiple learning
pathways while maintaining core competency development across essential domains.
Second, the research demonstrates a complex relationship between perceived difficulty,
engagement patterns, and learning outcomes. The discovery of an optimal engagement zone and
the observation that higher perceived difficulty often correlates with increased persistence rather
than decreased engagement provides valuable guidance for platform design and pedagogical
approaches.
Third, the analysis of category-based learning patterns reveals that analytical challenges may
serve as an effective entry point for cybersecurity education, with technical challenges requiring
more structured support and scaffolding. This insight can inform the design of more effective
learning progressions in cybersecurity education platforms.
While this study has limitations, including sample size and demographic constraints, it provides a
foundation for future research in cybersecurity education. Future studies should explore
longitudinal learning patterns, investigate team-based learning dynamics, and examine the impact
of cultural factors on cybersecurity skill development. This research contributes to the ongoing
development of evidence-based approaches to cybersecurity education, ultimately supporting the
preparation of the next generation of cybersecurity professionals.
ACKNOWLEDGEMENTS
The authors would like to thank Sultan Idris Education University for final support for this
research. The first author would also like to appreciate the research support provided by
University of Wollongong Malaysia during his research attachment when this article was written.
REFERENCES
[1] M. Katsantonis, A. Manikas, I. Mavridis, and D. Gritzalis, “Cyber range design framework for
cyber security education and training,” Mar. 18, 2023, Springer Science+Business Media. doi:
10.1007/s10207-023-00680-4.
[2] P. Šeda, J. Vykopal, V. Švábenský, and P. Čeleda, “Reinforcing Cybersecurity Hands-on Training
With Adaptive Learning,” Oct. 13, 2021. doi: 10.1109/fie49875.2021.9637252.
[3] M. Katsantonis, A. Manikas, I. Mavridis, and D. Gritzalis, “Cyber range design framework for
cyber security education and training,” Mar. 18, 2023, Springer Science+Business Media. doi:
10.1007/s10207-023-00680-4.
[4] K. Leune and S. J. Petrilli, “Using Capture-the-Flag to Enhance the Effectiveness of Cybersecurity
Education,” Sep. 27, 2017. doi: 10.1145/3125659.3125686.
[5] T. Goodman and A.-I. Radu, “Learn-Apply-Reinforce/Share Learning: Hackathons and CTFs as
General Pedagogic Tools in Higher Education, and Their Applicability to Distance Learning,” Jan.
01, 2020, Cornell University. doi: 10.48550/arxiv.2006.04226.

11
[6] O. Chernikova, N. Heitzmann, M. Stadler, D. Holzberger, T. Seidel, and F. Fischer, “Simulation-
Based Learning in Higher Education: A Meta-Analysis,” Jun. 15, 2020, SAGE Publishing. doi:
10.3102/0034654320933544.
[7] L. J. Khoo, “Design and Develop a Cybersecurity Education Framework Using Capture the Flag
(CTF),” Jun. 12, 2018, IGI Global. doi: 10.4018/978-1-5225-6026-5.ch005.
[8] P. L. da R. Rodrigues, L. P. Franz, J. F. P. Cheiran, J. P. S. da Silva, and A. S. Bordin, “Coding
Dojo as a transforming practice in collaborative learning of programming,” Sep. 18, 2017. doi:
10.1145/3131151.3131180.
[9] B. Grawemeyer, J. Halloran, M. England, and D. Croft, “Feedback and Engagement on an
Introductory Programming Module,” Dec. 27, 2021. doi: 10.1145/3498343.3498348.
[10] B. Yett, C. R. Snyder, N. Hutchins, and G. Biswas, “Exploring the Relationship Between
Collaborative Discourse, Programming Actions, and Cybersecurity and Computational Thinking
Knowledge,” Dec. 08, 2020. doi: 10.1109/tale48869.2020.9368459.
[11] M. H. Dunn and L. D. Merkle, “Assessing the Impact of a National Cybersecurity Competition on
Students’ Career Interests,” Feb. 21, 2018. doi: 10.1145/3159450.3159462.
[12] J. Msane, B. M. Mutanga, and T. Chani, “Students’ Perception of the Effect of Cognitive Factors in
Determining Success in Computer Programming: A Case Study,” Jan. 01, 2020, Science and
Information Organization. doi: 10.14569/ijacsa.2020.0110724.
[13] K. Aguar, S. Safaei, H. R. Arabnia, J. B. Gutiérrez, W. D. Potter, and T. R. Taha, “Reviving
Computer Science Education through Adaptive, Interest-Based Learning,” Dec. 01, 2017. doi:
10.1109/csci.2017.202.
[14] V. Švábenský et al., “Evaluating Two Approaches to Assessing Student Progress in Cybersecurity
Exercises,” Feb. 22, 2022. doi: 10.1145/3478431.3499414.
[15] M. Ellis, L. Baum, K. Filer, and S. H. Edwards, “Experience Report: Exploring the Use of CTF-
based Co-Curricular Instruction to Increase Student Comfort and Success in Computing,” Jun. 18,
2021. doi: 10.1145/3430665.3456376.
[16] R. W. Bell, E. Y. Vasserman, and E. C. Sayre, “Developing and Piloting a Quantitative Assessment
Tool for Cybersecurity Courses,” Jul. 08, 2015. doi: 10.18260/p.23835.
[17] Weidong Li, Amelia Lee, Melinda Solmon, “The role of perceptions of task difficulty in relation to
self-perceptions of ability, intrinsic value, attainment value, and performance.” Nov. 2023.
Accessed: Dec. 20, 2024. [Online]. Available:
https://journals.sagepub.com/doi/10.1177/1356336X07081797
[18] R. F. Kizilcec et al., “Scaling up behavioral science interventions in online education,” Jun. 15,
2020, National Academy of Sciences. doi: 10.1073/pnas.1921417117.
[19] D. Oyserman, K. Elmore, S. Novin, O. Fisher, and G. C. Smith, “Guiding People to Interpret Their
Experienced Difficulty as Importance Highlights Their Academic Possibilities and Improves Their
Academic Performance,” May 25, 2018, Frontiers Media. doi: 10.3389/fpsyg.2018.00781.
[20] D. Shoemaker, D. Davidson, and A. Conklin, “Toward a Discipline of Cyber Security: Some
Parallels with the Development of Software Engineering Education,” Dec. 02, 2017, Taylor &
Francis. doi: 10.1080/07366981.2017.1404867.
[21] M. Bishop et al., “Cybersecurity Curricular Guidelines,” in IFIP advances in information and
communication technology, Springer Science+Business Media, 2017, p. 3. doi: 10.1007/978-3-319-
58553-6_1.
[22] R. S. Baker, U. Boser, and E. L. Snow, “Learning engineering: A view on where the field is at,
where it’s going, and the research needed.,” Mar. 31, 2022, American Psychological Association.
doi: 10.1037/tmb0000058.
[23] T. Goodman and A.-I. Radu, “Learn-Apply-Reinforce/Share Learning: Hackathons and CTFs as
General Pedagogic Tools in Higher Education, and Their Applicability to Distance Learning,” Jan.
01, 2020, RELX Group (Netherlands). doi: 10.2139/ssrn.3637823.
[24] O. Chen, F. Paas, and J. Sweller, “A Cognitive Load Theory Approach to Defining and Measuring
Task Complexity Through Element Interactivity,” Jun. 01, 2023, Springer Science+Business Media.
doi: 10.1007/s10648-023-09782-w.

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AUTHORS
Khoo Li Jing is an academic in University of Wollongong Malaysia. His research
interests are in game-based learning, andcybersecurity skill development. He is
completing his Ph.D. in Game-Based Learning at Sultan Idris Education University,
Malaysia. He completed his M.Sc. in Computer Network Security from Liverpool
John Moores University, U.K., and his Bachelor in Information Systems from Tunku
Abdul Rahman University of Management and Technology, Malaysia.
Maizatul Hayati Mohamad Yatim is an associate professor of Human-Computer
Interaction in Computing Department, at Sultan Idris Education University,
Malaysia. Her research interests are specifically on game design and development,
game usability, and game-based learning. She received her Ph.D. in Computer
Science from Otto-von-Guericke University of Magdeburg, Germany, her M.Sc. in
Information Technology, and her Bachelor in Information Technology both from
Northern University of Malaysia.
Ts. Dr. Wong Yoke Seng is the Deputy Director (Data & Strategic Planning), of the
Research Management Centre in Universiti Perguruan Sultan Idris. His specialisation
is in Game-based Learning. He received a B.S degree in Information System
Engineering from Campbell University and M.S.C in Computer Game Technology
from Liverpool John Moores University, UK. He has been teaching game design and
game programming related subjects for over 20 years and actively involved in game
related research projects. His current research involves using computer game as
learning tool for learning object-oriented programming in tertiary level and his current
interests are gamification, game design, data structures & algorithms and object
oriented system analysis and design. Malaysia

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